Been wrestling with this for a while—finally got our Brand Voice in Writesonic to sound like us, not a robot reading a corporate handbook. The trick isn't just feeding it a style guide PDF. You have to get specific with examples and context.
Here’s what worked for our team:
* **Source Material Matters:** Don't just use your website copy. Gather real examples:
* Internal team communications (Slack snippets about wins)
* Customer support replies that got great feedback
* Social media posts with high engagement
* Email newsletter sections that people actually quoted back to you
* **Define the "Anti-Voice":** Be explicit about what you *don't* want. I gave it directives like:
* Avoid phrases like "leverage," "synergy," "cutting-edge solution."
* No passive voice. We say "We built this," not "This was built."
* Never sound overly formal or congratulatory without substance.
The most effective step was using the **"Improve"** and **"Rephrase"** tools on our own existing good content. I'd paste a paragraph we liked, ask Writesonic to rephrase it in several ways, and then explicitly tell the AI *which version was wrong and why*. This iterative feedback seems to train it faster.
For technical setups, if you're using the API or connecting via Zapier to feed content, structure your data payload with a clear `voice_instructions` field. Example:
```json
{
"content": "Draft a blog intro about our new feature.",
"instructions": {
"voice": "friendly_expert",
"key_points": ["direct", "use analogies", "acknowledge user pain points"],
"avoid": ["hyperbole", "jargon", "exclamation marks"]
}
}
```
It took about 50-60 of these guided iterations across different content types (short social posts, longer blog outlines) before the outputs consistently felt authentic. Start small—get it right for one use case before expecting it to handle everything.
hth
The "improve and rephrase" method is spot on. I do something similar when tuning CI/CD pipeline descriptions in our internal docs.
Treating it like a linter for tone - you keep feeding it corrections until the automated style check passes. Makes the training tangible.
Ship it, but test it first
That's a great approach. Using the "Improve" and "Rephrase" tools as a feedback loop is clever - it's basically giving the model direct corrections.
I'd add that it helps to tag your examples. When you're feeding it those Slack snippets or support replies, preface them with a short descriptor of the context and goal. Something like:
* **Tag:** "Reply to frustrated customer - empathetic but direct"
* **Tag:** "Internal announcement - concise and energetic"
It gives the AI more signal than just raw text. It starts to learn not just *what* you say, but the intent behind it in different situations.
Sounds great until you get the bill for the compute. All that iterative training with "improve and rephrase" on proprietary content? That's how they get you on the high-tier plan. You're paying them to be a copywriting tutor.
What's the ROI when the platform changes its model and you have to start over?
Your stack is too complicated.
That's really helpful, thanks. Using the "improve and rephrase" tool as direct feedback makes a lot of sense. It's like training a linter, like someone else said.
I'm curious, how much example content did you need to feed it before you started seeing consistent results? Was there a sweet spot, or did you just keep going until it clicked?
CloudNewbie
The "anti-voice" directive is a brilliant, practical step that gets overlooked. Being explicit about what to avoid cuts through so much ambiguity. I'd add that it's useful to periodically revisit that list, as teams can accidentally develop new jargon cliches over time.
Your method with the "Improve" tool essentially creates a feedback loop, which is smart. Have you found that the model starts to generalize those corrections, or do you still have to catch the same type of phrasing repeatedly?
Keep it constructive.
Oh, tagging examples by intent is such a good idea! It makes sense that "empathetic but direct" gives more signal than just the text.
But I'm new to this - when you tag things, do you use just a few standard tags or do they get super specific? Like, would you have a different tag for "reply to frustrated customer" vs. "reply to confused customer"?
Great question on tagging specificity. I've found it's a balancing act. You need enough distinct tags to teach the model different tones, but not so many that it can't generalize.
A few broad, high-intent categories work well as your foundation. "Frustrated" and "confused" could actually live under a parent tag like "customer_issue_resolution". The nuanced tone comes from your tagged examples. The model learns the subtle difference between a snippy reply and a patient one from the actual text you provide.
Where I get more specific is with content types where the goal is radically different, like "blog_post_thought_leadership" vs "support_documentation".